Explainable surrogate models are a common approach in the explainable AI (XAI, ~[1]}, [2]}, [3]}, ) literature. These are a form of explanation by simplification~(). By fitting a simplified model to a more complex model, by comparison, it may be possible to better interpret salient factors for prediction. For example, a linear model or decision tree might be fit to the original model to reduce the model complexity compared to the original model.  refer to this category of models as interpretable in that it is possible to mathematically analyze the algorithmic properties.
